A personalized approach when you visit a website. When you are on an e-commerce site or using a search engine, the host collects rich information on your behavior. Machine learning analyzes the data and transforms the website into something geared to the individual customer. Machine learning then will control what you see, what appears in a search bar, how the site communicates with you, to best meet your individual needs.

Making recommendations. Making recommendations relevant for the user was one of the first major consumer applications of machine learning. Virtually everyone has experienced Amazon’s recommendations, when you make a purchase it recommends products likely to resonate (and almost everyone has taken advantage of these recommendations). Automated personalization with machine learning takes information about the shopper, refines those recommendations and tailors them specifically to the individual shopper. As the article points out, “it is like having a salesperson with the customer the whole time, pointing out what products he or she thinks are right up the customer’s alley.”

Customer profiles. Machine learning can extend personalization further, by taking a customer’s life event and turning it into messaging or a special offer. If a user updates their Facebook profile to show they are engaged, machine learning may direct an offer encouraging the customer to get their fiancé using the product.

Predicting preferences. Machine learning algorithms can help predict consumers’ preferences, whether it is the music they want to hear, the movies they would like to watch or the chocolate they would like to try. The company can then display products or services (or games or in-game purchases) that are interesting to the consumer, giving them a more relevant experience. While like the recommendation engines, predicting preferences is more about customizing the entire shopping experience (imagine walking into Macy’s and all the products you see are ones that would be interesting to you).

Wearables. A very interesting part of the tech sector that I have not written about often is wearables and machine learning is deeply integrated with the wearables opportunity. You can collect massive amounts of data from users who are sporting wearables. Machine learning can take this data and customize future interactions with the user based on their behavior.

Leveraging machine learning

As these examples show, there are many applications of machine learning to make a more personalized experience for users. While it is crucial to take into account the user’s privacy, giving people an experience tailored to their needs saves them time and is more interesting than a generic encounter.

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Author: Lloyd Melnick

I am EVP Casino at VGW, where I lead the Chumba Casino team. Previously, I was Director of StarsPlay, the social gaming vertical for the Stars Group (PokerStars, Sky Betting & Gaming, BetEasy, Full Tilt and BetStars). I was also Sr Dir at Zynga's social casino (including Hit It Rich! slots, Zynga Poker and our mobile games), where I led VIP CRM efforts and arranged licensing deals. I have been a central part of the senior management team (CCO, GM and CGO) at three exits (Merscom/Playdom, Playdom/Disney and Spooky Cool/Zynga) worth over $700 million.
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Lloyd Melnick

This is Lloyd Melnick’s personal blog. I am EVP Casino at VGW, where I lead the Chumba Casino team. I am a serial builder of businesses (senior leadership on three exits worth over $700 million), successful in big (Disney, Stars Group, Zynga) and small companies (Merscom, Spooky Cool Labs) with over 20 years experience in the gaming and casino space.